




"""simple docstring"""




# Lint as: python3
import itertools
import os
import re


lowerCAmelCase				:     int					=				re.compile(r"""([A-Z]+)([A-Z][a-z])""")
lowerCAmelCase				:     Any					=				re.compile(r"""([a-z\d])([A-Z])""")

lowerCAmelCase				:     Dict					=				re.compile(r"""(?<!_)_(?!_)""")
lowerCAmelCase				:     List[Any]					=				re.compile(r"""(_{2,})""")

lowerCAmelCase				:     List[Any]					=				r"""^\w+(\.\w+)*$"""

lowerCAmelCase				:     Optional[Any]					=				r"""<>:/\|?*"""



def        a__					(							snake_case__							)		->   str:
			lowerCamelCase      =   _uppercase_uppercase_re.sub(R"""\1_\2"""			,					snake_case__							)
			lowerCamelCase      =   _lowercase_uppercase_re.sub(R"""\1_\2"""			,					snake_case__							)
			return name.lower()



def        a__					(							snake_case__							)		->   List[str]:
			lowerCamelCase      =   _single_underscore_re.split(snake_case__							)
			lowerCamelCase      =   [_multiple_underscores_re.split(snake_case__							) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(snake_case__							) if n != """"""							)



def        a__					(							snake_case__							)		->   List[str]:
			if os.path.basename(snake_case__							) != name:
						raise ValueError(F'Should be a dataset name, not a path: {name}'							)
			return camelcase_to_snakecase(snake_case__							)



def        a__					(							snake_case__			,					snake_case__							)		->   str:
			if os.path.basename(snake_case__							) != name:
						raise ValueError(F'Should be a dataset name, not a path: {name}'							)
			if not re.match(_split_re			,					snake_case__							):
						raise ValueError(F'Split name should match \'{_split_re}\'\' but got \'{split}\'.'							)
			return F'{filename_prefix_for_name(snake_case__							)}-{split}'



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__=None							)		->   Tuple:
			lowerCamelCase      =   filename_prefix_for_split(snake_case__			,					snake_case__							)
			if filetype_suffix:
						prefix += F'.{filetype_suffix}'
			lowerCamelCase      =   os.path.join(snake_case__			,					snake_case__							)
			return F'{filepath}*'



def        a__					(							snake_case__			,					snake_case__			,					snake_case__			,					snake_case__=None			,					snake_case__=None							)		->   Union[str, Any]:
			lowerCamelCase      =   filename_prefix_for_split(snake_case__			,					snake_case__							)
			lowerCamelCase      =   os.path.join(snake_case__			,					snake_case__							)

			if shard_lengths:
						lowerCamelCase      =   len(snake_case__							)
						lowerCamelCase      =   [F'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(snake_case__							)]
						if filetype_suffix:
									lowerCamelCase      =   [filename + F'.{filetype_suffix}' for filename in filenames]
						return filenames
			else:
						lowerCamelCase      =   prefix
						if filetype_suffix:
									filename += F'.{filetype_suffix}'
						return [filename]









"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        __magic_name__				(			UpperCAmelCase__		,   unittest.TestCase							):






				'''simple docstring'''
				__UpperCamelCase         =		DiTPipeline
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase         =		PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				__UpperCamelCase         =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase         =		False





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							torch.manual_seed(0					)
							lowerCamelCase      =   TransformeraDModel(
							    sample_size=16						,					num_layers=2						,					patch_size=4						,					attention_head_dim=8						,					num_attention_heads=2						,					in_channels=4						,					out_channels=8						,					attention_bias=_a						,					activation_fn="""gelu-approximate"""						,					num_embeds_ada_norm=1_000						,					norm_type="""ada_norm_zero"""						,					norm_elementwise_affine=_a						,					)
							lowerCamelCase      =   AutoencoderKL()
							lowerCamelCase      =   DDIMScheduler()
							lowerCamelCase      =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
							return components





				def   _lowerCAmelCase							(			self						,					_a						,					_a=0					):





							"""simple docstring"""
							if str(_a					).startswith("""mps"""					):
										lowerCamelCase      =   torch.manual_seed(_a					)
							else:
										lowerCamelCase      =   torch.Generator(device=_a					).manual_seed(_a					)
							lowerCamelCase      =   {
							    """class_labels""": [1],
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   """cpu"""

							lowerCamelCase      =   self.get_dummy_components()
							lowerCamelCase      =   self.pipeline_class(**_a					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

							lowerCamelCase      =   self.get_dummy_inputs(_a					)
							lowerCamelCase      =   pipe(**_a					).images
							lowerCamelCase      =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape						,					(1, 16, 16, 3)					)
							lowerCamelCase      =   np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]					)
							lowerCamelCase      =   np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(_a						,					1e-3					)





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_inference_batch_single_identical(relax_max_difference=_a						,					expected_max_diff=1e-3					)





				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()						,					reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,					)
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






@require_torch_gpu
@slow
class        __magic_name__				(			unittest.TestCase							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   torch.manual_seed(0					)

							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=40						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'					)
										assert np.abs((expected_image - image).max()					) < 1e-2





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""					)
							lowerCamelCase      =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
							pipe.to("""cuda"""					)

							lowerCamelCase      =   ["""vase""", """umbrella"""]
							lowerCamelCase      =   pipe.get_label_ids(_a					)

							lowerCamelCase      =   torch.manual_seed(0					)
							lowerCamelCase      =   pipe(_a						,					generator=_a						,					num_inference_steps=25						,					output_type="""np"""					).images

							for word, image in zip(_a						,					_a					):
										lowerCamelCase      =   load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    f'/dit/{word}_512.npy'					)

										assert np.abs((expected_image - image).max()					) < 1e-1



